Enterprise account-based marketing has spent the better part of a decade trapped in a paradox. The discipline depends on coordinated data flowing between platforms (intent providers, CRMs, marketing automation systems, advertising networks, sales engagement tools) yet each platform speaks its own dialect. The result: ABM programs that look sophisticated in vendor demos but fragment in production. Now, a proposed Open Standards Initiative (OSI) for ABM data aims to create a shared schema across the ecosystem. If it gains adoption, OSI could do for account-based orchestration what DMARC did for email authentication or what OpenRTB did for programmatic advertising. If it does not, it joins a long list of well-intentioned interoperability projects that died in committee. The distinction matters enormously for enterprise marketing operations teams already managing five, ten, or twenty integrations between their revenue tools.
1. Historical context
Account-based marketing, as a named practice, dates to the early 2010s. The ITSMA (now Momentum ITSMA) formalized the term around 2004, but ABM's explosive growth coincided with the emergence of a vendor category: Demandbase launched its advertising product in 2012, Terminus followed in 2014, and 6sense began pivoting toward predictive account intelligence around the same time. By 2016, SiriusDecisions (now part of Forrester) declared ABM one of the fastest-adopted strategies in B2B marketing history.
The problem was structural from the start. Each ABM platform built its own account graph, its own scoring methodology, its own intent taxonomy. Demandbase defined "intent" differently than Bombora, which defined it differently than 6sense's proprietary model. When enterprises tried to connect these signals to their marketing automation platforms (Oracle Eloqua, Adobe Marketo Engage, HubSpot, Salesforce Marketing Cloud), they discovered that account-level data and lead-level data lived in incompatible ontologies.
The industry's response was a proliferation of point-to-point integrations. A typical enterprise ABM stack in 2020 might include a native Marketo-to-Demandbase connector, a Salesforce-to-6sense integration via middleware, a separate data warehouse feeding Bombora intent signals into an analytics layer, and a custom CSV import for firmographic enrichment. Each connector mapped fields differently. Each had its own refresh cadence. None of them agreed on what an "engaged account" actually meant.
This is the context in which OSI arrives. As we explored in our analysis of the data layer beneath campaign failures, the root cause of most broken ABM programs is not strategy. It is plumbing. OSI is an attempt to standardize that plumbing at the schema level, creating a common data model for account signals, engagement scoring, and buying stage definitions.
The precedent that matters here is not another MarTech standard. It is the evolution of financial data exchange. Before FIX (Financial Information eXchange) protocol became standard in the 1990s, every brokerage transmitted trade data in proprietary formats. FIX did not eliminate competition between platforms. It made them interoperable. OSI aspires to the same outcome for ABM data.
Source: ChiefMartec / MartechTribe 2024 Stackie Awards analysis and Martech Replacement Survey
"The average enterprise has 91 marketing cloud services... but the real problem isn't the number of tools. It's the number of integration points between them."
2. Technical analysis
OSI's proposed architecture addresses three specific layers of the ABM data problem, each of which has distinct implications for enterprise integration teams.
The account identity layer
The most fundamental challenge in ABM is account resolution: determining that a cluster of IP addresses, cookie signals, email domains, and CRM records all refer to the same buying organization. Today, each vendor maintains a proprietary account graph. Demandbase uses a combination of IP intelligence and first-party CRM matching. 6sense relies on its own AI model to de-anonymize traffic. Clearbit (now part of HubSpot) builds company profiles from web crawling and public data.
OSI proposes a shared account identifier schema. This does not mean a single universal ID (an idea that has failed repeatedly, most recently with third-party cookie deprecation). Instead, it defines a standard way to express and cross-reference multiple identifiers: CRM account ID, DUNS number, domain, and vendor-specific identifiers can all be mapped into a common envelope. Think of it as a Rosetta Stone for account identity rather than a replacement for any single identifier.
For teams managing platform integrations across multiple systems, this is significant. Today, reconciling accounts between, say, Oracle Eloqua and 6sense requires custom field mapping, deduplication logic, and often a middleware layer like Workato or Tray.io. A standardized identity schema would reduce this to a configuration step rather than a development project.
The signal taxonomy layer
The second layer addresses what OSI calls "signal classification." An intent signal from Bombora (based on content consumption across its publisher cooperative) and an engagement signal from Marketo (based on email opens, form fills, and web visits) measure different things. But in most ABM implementations, they get flattened into a single "account score" without preserving their distinct provenance.
OSI proposes a taxonomy that classifies signals by source type (first-party behavioral, third-party intent, firmographic, technographic), recency, confidence level, and decay rate. This is a direct response to a problem that David Raab of the CDP Institute has written about extensively: the tendency of ABM platforms to present composite scores that obscure the underlying signal quality.
From a data management perspective, this taxonomy would enable marketing operations teams to build scoring models that weight signals differently based on their classification. A first-party form submission from a target account should carry more weight than a third-party intent signal with a low confidence score. Today, achieving this requires custom logic in each platform. A shared taxonomy makes it portable.
The orchestration event layer
The third layer is perhaps the most ambitious. OSI proposes a standard event format for orchestration actions: when one system decides to change an account's stage, suppress it from an advertising audience, or trigger a sales alert, it should emit an event in a format that any other system in the stack can consume.
This is where OSI moves beyond data standardization into workflow interoperability. If Demandbase detects that a target account has entered a "decision" stage and emits a standardized event, Marketo could consume that event to trigger a late-stage nurture program, Salesforce could update the opportunity record, and a conversational marketing tool like Drift could adjust its playbook. All without custom integration code for each pair of systems.
The technical model draws from event-driven architectures common in software engineering (Apache Kafka, AWS EventBridge) but applied to the specific vocabulary of B2B buying journeys. For enterprise teams accustomed to batch-based data syncs between their ABM and automation platforms, this represents a shift toward real-time, event-driven orchestration.
3. Strategic implications
The question enterprise marketing operations leaders should ask is not whether OSI is technically sound. It probably is. The question is whether the vendor ecosystem has sufficient incentive to adopt it.
The vendor incentive problem
ABM platform vendors derive competitive advantage from proprietary data. 6sense's valuation (reportedly over $5 billion after its 2021 funding round) rests substantially on the distinctiveness of its account graph and predictive models. Demandbase, Bombora, and others occupy similar positions. Asking these vendors to expose their data through a common schema is like asking search engines to share their ranking algorithms.
The counter-argument is that standardization usually expands markets rather than shrinking them. OpenRTB did not destroy programmatic advertising vendors. It created a much larger ecosystem by reducing the friction of programmatic buying. Similarly, OSI could expand the total addressable market for ABM tools by making it easier for enterprises to adopt multi-vendor strategies.
But the analogy has limits. Programmatic advertising had a natural forcing function: the buy side (agencies and brands) had enormous collective purchasing power and demanded interoperability. In ABM, the buy side (enterprise marketing teams) is fragmented, and many are locked into single-vendor contracts. The forcing function is weaker.
Implications for platform selection
For enterprises evaluating their marketing automation strategy, OSI's trajectory matters. If the standard gains traction, the value of a marketing automation platform increasingly depends on its ability to consume and emit standardized events rather than on the depth of its proprietary integrations with specific ABM vendors.
This favors platforms with strong API ecosystems and event-driven architectures. Marketo's webhook capabilities and custom object model give it flexibility here. Oracle Eloqua's App Cloud ecosystem and its integration with Oracle's broader data infrastructure offer another path. HubSpot's Operations Hub, with its custom-coded workflows, could adapt quickly. Salesforce Marketing Cloud's position within the broader Salesforce ecosystem (particularly Data Cloud, formerly Genie) gives it a natural advantage in account-level data unification.
The implication for platform strategy is clear: the era of evaluating marketing automation tools based on their native ABM integrations may be ending. What matters instead is a platform's ability to participate in an open data ecosystem. Teams conducting a platform maturity assessment should add event-driven architecture readiness to their evaluation criteria.
The CDP question
OSI also complicates the customer data platform conversation. CDPs like Segment, Tealium, and Treasure Data have positioned themselves as the unifying layer for customer data. If OSI succeeds, it creates a parallel unification layer specific to ABM data. This raises a question: does your ABM orchestration data belong in your CDP, in a dedicated ABM layer that speaks OSI, or in both?
The answer likely depends on organizational structure. Companies where ABM is owned by marketing operations will probably route ABM data through their CDP or marketing automation platform. Companies where ABM is a cross-functional discipline spanning marketing, sales, and customer success may benefit from a dedicated orchestration layer that emits standardized events to all downstream systems. As we analyzed in the broken stack problem, the technology architecture should follow the operating model, not the other way around.
"ABM has always been a data management discipline disguised as a marketing strategy."
4. Practical application
Enterprise teams should not wait for OSI to achieve full industry adoption before acting. The principles behind the standard can guide integration architecture decisions today.
Audit your current account identity resolution
Map every system in your stack that maintains an account record. Document which identifiers each system uses as its primary key. In most enterprises, this audit reveals that the CRM uses one account ID, the ABM platform uses a proprietary ID, the marketing automation platform uses a combination of email domain and company name, and the data warehouse uses a DUNS number or a custom composite key.
Once mapped, define a canonical account identifier hierarchy. This does not require OSI. It requires a decision: which identifier is authoritative, and how do other identifiers map to it? Most organizations find that the CRM account ID is the most practical canonical identifier, with domain-based matching as a fallback for records not yet associated with a CRM account. Teams investing in data normalization should make account identity resolution a first-order concern.
Classify your signals before you score them
Borrow OSI's signal taxonomy concept, even in a simplified form. Create a classification for every signal that feeds your account scoring model. At minimum, tag each signal with its source type (first-party vs. third-party), its latency (real-time vs. batch), and its confidence level (deterministic vs. probabilistic).
This classification exercise often reveals uncomfortable truths. Many enterprises discover that their "high-intent" account scores are driven primarily by third-party probabilistic signals with weak confidence levels, while first-party deterministic signals (a target account submitting a demo request form, for example) carry the same weight in the composite score. Fixing this does not require new technology. It requires a scoring model that respects signal provenance. This is where a thoughtful lead scoring framework becomes essential.
Prototype event-driven orchestration in one workflow
Pick a single ABM workflow, such as the transition from "aware" to "engaged" stage, and redesign it using event-driven principles. Instead of batch-syncing account stages nightly from your ABM platform to your CRM and marketing automation tool, configure a webhook or API-based trigger that fires when the stage change occurs.
In Marketo, this can be implemented using webhooks that listen for stage-change events from your ABM platform and trigger corresponding smart campaigns. In Oracle Eloqua, Program Builder canvas steps can be configured to consume external events via Eloqua's API or cloud app connectors. The goal is to prove the concept with one workflow before extending event-driven orchestration across the full buying journey.
Establish a data contract with your ABM vendor
Regardless of OSI's adoption timeline, you can negotiate data contracts with your ABM vendors today. A data contract specifies the schema, format, refresh cadence, and quality guarantees for data that flows between systems. If your intent data provider sends you a weekly batch file with inconsistent field formatting, that is not an integration. It is a liability.
Define what "good" looks like for each data feed: required fields, acceptable null rates, format specifications, and SLA for delivery. This is operational hygiene that pays dividends regardless of whether OSI becomes an industry standard.
5. Future scenarios
Two plausible trajectories emerge for OSI and the broader question of ABM data interoperability over the next 18 to 24 months.
Scenario one: partial adoption creates a two-tier ecosystem
In this scenario, OSI gains adoption among mid-market ABM tools and data providers eager to differentiate on interoperability, while the largest platforms (6sense, Demandbase) participate nominally but retain proprietary extensions that limit true portability. This is the most likely outcome based on historical precedent. It mirrors what happened with MACH Alliance principles in commerce technology: broad rhetorical support, uneven implementation.
For enterprise teams, this scenario means that OSI-compliant tools become attractive for specific use cases (particularly intent data aggregation and signal classification) while the core ABM platform remains a proprietary system. The middleware layer (CDPs, iPaaS tools, data warehouses) becomes even more important as the translation layer between OSI-compliant and non-compliant systems.
The practical implication: invest in your middleware and data layer now. As we discussed in the analytics maturity gap, the organizations that win are those with a strong data foundation that can adapt to shifting standards, not those locked into any single vendor's integration model.
Scenario two: a major platform acquisition accelerates standardization
If one of the large marketing cloud vendors (Salesforce, Adobe, Oracle, or HubSpot) acquires a company involved in OSI's development, the standard could gain rapid de facto adoption. Salesforce's acquisition of MuleSoft in 2018 is a useful analogy. MuleSoft's integration philosophy became embedded in Salesforce's architecture, and its API-led connectivity approach became an industry norm within a few years.
A similar acquisition in the ABM space could make OSI compliance a checkbox requirement for the entire ecosystem. If, say, Adobe embedded OSI-compliant event schemas into Marketo's architecture, every ABM vendor would need to support the standard to maintain their Marketo integration. This is the fastest path to adoption, but it depends on unpredictable corporate strategy decisions.
For enterprise teams, this scenario argues for maintaining platform flexibility. Organizations locked into rigid, custom-coded integrations with a single ABM vendor would face expensive rewiring. Those with modular, API-first integration architectures would adapt quickly. This is another argument for treating platform integrations as a strategic capability rather than a technical afterthought.
The AI variable
Both scenarios are complicated by the rapid advance of AI in marketing operations. Large language models and AI agents are increasingly capable of translating between data formats, inferring schema mappings, and orchestrating workflows across systems with different data models. If AI-powered integration becomes sophisticated enough, the need for a formal standard like OSI diminishes. The AI becomes the universal translator.
This is technically feasible but operationally risky. AI-mediated integration introduces a black box into the data pipeline, one that is difficult to audit, debug, or govern. For enterprises subject to data privacy regulations, running ABM data through AI translation layers without clear schema governance creates compliance exposure. OSI and AI integration are not mutually exclusive. The standard provides the governance framework; AI provides the execution flexibility.
6. Takeaways
- OSI addresses a genuine structural problem: the absence of a shared data model for account identity, signal classification, and orchestration events across ABM tools. The problem has persisted for a decade and has real revenue consequences.
- Vendor adoption incentives are mixed. Large ABM platforms benefit from proprietary data moats. Standardization is more likely to be driven by platform vendors (the marketing automation and CRM companies) or by acquisition dynamics than by voluntary cooperation among ABM tool makers.
- Enterprise teams should not wait for OSI to act. The principles of canonical account identity, signal taxonomy, and event-driven orchestration can be implemented today within existing stack architectures.
- The middleware and data layer is where the real leverage sits. Whether OSI succeeds or not, organizations with strong data foundations and modular integration architectures will adapt faster than those with brittle, point-to-point integrations.
- AI-powered integration and formal standards like OSI are complementary, not competing. Standards provide governance and auditability. AI provides flexibility and speed. Enterprise teams need both.
- Platform selection criteria should evolve. The ability to participate in open data ecosystems (strong APIs, webhook support, event-driven architecture, custom object models) is becoming more important than the depth of any single native integration.
